A Prediction on the Effect of the Proposed Triboro Line on the Transportation Patterns throughout New York City

Timothy Miller

Department of Geography and Environmental Science, Hunter College

GTECH 70500: Spatial Data Analysis

Professor Jochen Albrecht

December 13, 2022

Outline of Triboro Line proposed by the Regional Plan Association

Outline of Triboro Line proposed by the Regional Plan Association

Conceptual model of transportation dynamics

Conceptual model of transportation dynamics

NYC Boroughs served by the MTA subway system

NYC Boroughs served by the MTA subway system

Number of jobs in each NTA across the Boroughs of Interest

Number of jobs in each NTA across the Boroughs of Interest

Desire lines for trips across the Boroughs of interest with at least 5000 trips are made

Desire lines for trips across the Boroughs of interest with at least 5000 trips are made

Number of jobs where the trip origin is within the same borough.

Number of jobs where the trip origin is within the same borough.

Desire lines for the most popular trips that start and end within the borough

Desire lines for the most popular trips that start and end within the borough

Frequency of job counts in each NTAs, standardized by the area of the NTA.

Frequency of job counts in each NTAs, standardized by the area of the NTA.

Frequency of job counts in each NTAs, standardized by the area of the NTA and tranformed with a natural log.

Frequency of job counts in each NTAs, standardized by the area of the NTA and tranformed with a natural log.

Number of jobs in each NTA tract. Job count is calculated from the sum of all trips which end in that NTA. It is standardized by the total area of the NTA. It is also transformed with a natural log.

Number of jobs in each NTA tract. Job count is calculated from the sum of all trips which end in that NTA. It is standardized by the total area of the NTA. It is also transformed with a natural log.

Global auto-correlation of job count using subway transit time for neighborhood wieghts

Global auto-correlation of job count using subway transit time for neighborhood wieghts

Global auto-correlation of job count using driving time for neighborhood wieghts

Global auto-correlation of job count using driving time for neighborhood wieghts

Global auto-correlation of job count using walking time for neighborhood wieghts

Global auto-correlation of job count using walking time for neighborhood wieghts

Global auto-correlation of job count using queens contiguity for neighborhood wieghts

Global auto-correlation of job count using queens contiguity for neighborhood wieghts

Local auto-correlation for job count of NTAs. Each map utilizes a unique neighborhood weighting. Top left uses queen contiguity. Top right uses driving time in traffic and is overlayed with major roads. Bottom left uses walking time. Bottom right uses transit time on the subway and is overlayed with the subway network.

Local auto-correlation for job count of NTAs. Each map utilizes a unique neighborhood weighting. Top left uses queen contiguity. Top right uses driving time in traffic and is overlayed with major roads. Bottom left uses walking time. Bottom right uses transit time on the subway and is overlayed with the subway network.

Frequency of commute counts, standardized by the sum of the areas of the commute's origin and destination NTAs

Frequency of commute counts, standardized by the sum of the areas of the commute’s origin and destination NTAs

Frequency of the natural log of commute counts, standardized by the sum of the areas of the commute's origin and destination NTAs

Frequency of the natural log of commute counts, standardized by the sum of the areas of the commute’s origin and destination NTAs

Boxplot for the number of commutes, factored by the number of subway lines used during the commute. Commutes are transformed by a natural log and standardized by area

Boxplot for the number of commutes, factored by the number of subway lines used during the commute. Commutes are transformed by a natural log and standardized by area

Time in transit on the subway plotted against the number of commutes standardized by area. A smoother line is overlayed in blue.

Time in transit on the subway plotted against the number of commutes standardized by area. A smoother line is overlayed in blue.

Time in subway transit is transormed by raising it to the power of one-eighth and taking the inverse. Commute count is transformed with the natural log. The values are plotted against each other. A smoother line is overlayed in blue.

Time in subway transit is transormed by raising it to the power of one-eighth and taking the inverse. Commute count is transformed with the natural log. The values are plotted against each other. A smoother line is overlayed in blue.

Diagnostic plots for the model of transformed subway transit time

Diagnostic plots for the model of transformed subway transit time

Time driving in traffic plotted against the number of commutes standardized by area. A smoother line is overlayed in blue.

Time driving in traffic plotted against the number of commutes standardized by area. A smoother line is overlayed in blue.

Time driving in traffic is transormed by raising it to the power of one-eighth and taking the inverse. Commute count is transformed with the natural log. The values are plotted against each other. A smoother line is overlayed in blue.

Time driving in traffic is transormed by raising it to the power of one-eighth and taking the inverse. Commute count is transformed with the natural log. The values are plotted against each other. A smoother line is overlayed in blue.

Diagnostic plots for the model of transformed driving time

Diagnostic plots for the model of transformed driving time

Time spent walking plotted against the number of commutes standardized by area. A smoother line is overlayed in blue.

Time spent walking plotted against the number of commutes standardized by area. A smoother line is overlayed in blue.

Time spent walking is transormed by raising it to the power of one-eighth and taking the inverse. Commute count is transformed with the natural log. The values are plotted against each other. A smoother line is overlayed in blue.

Time spent walking is transormed by raising it to the power of one-eighth and taking the inverse. Commute count is transformed with the natural log. The values are plotted against each other. A smoother line is overlayed in blue.

Diagnostic plots for the model of transformed walking time

Diagnostic plots for the model of transformed walking time

Models for transporation modes. Each model is only plotted from their minimum to maximum observed travel time.

Models for transporation modes. Each model is only plotted from their minimum to maximum observed travel time.

Models of transportation modes, zoomed on the range of 12min to 50mins. This range is seen in the travel time values for each transportation mode.

Models of transportation modes, zoomed on the range of 12min to 50mins. This range is seen in the travel time values for each transportation mode.

Hypothetical NTA layout

Hypothetical NTA layout

Hypothetical NTA network

Hypothetical NTA network

Matrix for hypothetical NTA network

Matrix for hypothetical NTA network

Complement for hypothetical NTA network

Complement for hypothetical NTA network

Global auto-correlation of commute network

Global auto-correlation of commute network

Local autocorrelation for commute network. Top left map shows all confidence types on one map. Top right shows Insignificant clusters. Middle left shows High High clusters. Middle right shows High Low clusters. Bottom left shows Low High clusters. Bottom right shows Low Low clusters

Local autocorrelation for commute network. Top left map shows all confidence types on one map. Top right shows Insignificant clusters. Middle left shows High High clusters. Middle right shows High Low clusters. Bottom left shows Low High clusters. Bottom right shows Low Low clusters

Graph of the top 5% most popular commutes through brooklyn and their interactions with each other. The color of the node reflects its membership in one of three clusters

Graph of the top 5% most popular commutes through brooklyn and their interactions with each other. The color of the node reflects its membership in one of three clusters

Geospatial distribution of the top 5% most popular commutes through brooklyn. Left diagram is the confidence type of the commute. Right diagram is commute's membership in the network cluster

Geospatial distribution of the top 5% most popular commutes through brooklyn. Left diagram is the confidence type of the commute. Right diagram is commute’s membership in the network cluster